A neural network model of mathematics anxiety: The role of attention.

Anxiety about performing numerical calculations is becoming an increasingly important issue. Termed mathematics anxiety, this condition negatively impacts performance in numerical tasks which can affect education outcomes and future employment. The disruption account proposes poor performance is due to anxiety disrupting limited attentional and inhibitory resources leaving fewer cognitive resources for the current task. This study provides the first neural network model of math anxiety. The model simulates performance in two commonly-used tasks related to math anxiety: the numerical Stroop and symbolic number comparison. Different model modifications were used to simulate high and low math-anxious conditions by modifying attentional processes and learning; these model modifications address different theories of math anxiety. The model simulations suggest that math anxiety is associated with reduced attention to numerical stimuli. These results are consistent with the disruption account and the attentional control theory where anxiety decreases goal-directed attention and increases stimulus-driven attention.

An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI.

Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further. Specifically, we used Spectral embedding, Multidimensional scaling, Isomap, t-Distributed Stochastic Neighbour Embedding, Uniform Manifold Approximation and Projection, and sparse denoising autoencoder based manifolds on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We then determine the clustering potential of the learned embeddings and then determine if category sub-groupings or sub-categories can be found. We then used a Kruskal-sWallis H test to determine the statistical significance of the discovered AD subcategories. Our results show that the existing AD categories do exhibit sub-groupings, especially in mild cognitive impairment transitions in many of the tested manifolds, showing there may be a need for further subcategories to describe AD progression.

Prediction of Site Directed miRNAs as Key Players of Transcriptional Regulators Against Influenza C Virus Infection Through Computational Approaches.

MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in gene expression, cell differentiation, and immunity against viral infections. In this study, we have used the computational tools, RNA22, RNAhybrid, and miRanda, to predict the microRNA-mRNA binding sites to find the putative microRNAs playing role in the host response to influenza C virus infection. This computational research screened the following four miRNAs: hsa-mir-3155a, hsa-mir-6796-5p, hsa-mir-3194-3p and hsa-mir-4673, which were further investigated for binding site prediction to the influenza C genome. Moreover, multiple sites in protein-coding region (HEF, CM2, M1-M2, NP, NS1- NS2, NSF, P3, PB1 and PB2) were predicted by RNA22, RNAhybrid and miRanda. Furthermore, 3D structures of all miRNAs and HEF were predicted and checked for their binding potential through molecular docking analysis. The comparative results showed that among all proteins, HEF is higher in prevalence throughout the analysis as a potential (human-derived) microRNAs target. The target-site conservation results showed that core nucleotide sequence in three different strains is responsible for potential miRNA binding to different viral strains. Further steps to use these microRNAs may lead to new therapeutic insights on fighting influenza virus infection.

A Synaptic Pruning-Based Spiking Neural Network for Hand-Written Digits Classification.

A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and collects the geometric features of the images from the Modified National Institute of Standards and Technology database (MNIST). In this work, a novel learning rule is developed to train the network to detect features of different digit classes. For this purpose, randomly initialized synaptic weights between the first and second layers are updated using average firing rates of pre- and postsynaptic neurons. Then, using a neuroscience-inspired mechanism named, "synaptic pruning" and its predefined threshold values, some of the synapses are deleted. Hence, these sparse matrices named, "information channels" are constructed so that they show highly specific patterns for each digit class as connection matrices between the first and second layers. The "information channels" are used in the test phase to assign a digit class to each test image. In addition, the role of feed-back inhibition as well as the connectivity rates of the second and third neural layers are studied. Similar to the abilities of the humans to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to the conventional deep learning methods that have shown a very good performance on the MNIST dataset. This work introduces a new class of brain-inspired spiking neural networks to extract the features of complex data images.

Vaccine versus Variants (3Vs): Are the COVID-19 Vaccines Effective against the Variants? A Systematic Review.

BACKGROUND: With the emergence and spread of new SARS-CoV-2 variants, concerns are raised about the effectiveness of the existing vaccines to protect against these new variants. Although many vaccines were found to be highly effective against the reference COVID-19 strain, the same level of protection may not be found against mutation strains. The objective of this study is to systematically review relevant studies in the literature and compare the efficacy of COVID-19 vaccines against new variants. METHODS: We conducted a systematic review of research published in Scopus, PubMed, and Google Scholar until 30 August 2021. Studies including clinical trials, prospective cohorts, retrospective cohorts, and test negative case-controls that reported vaccine effectiveness against any COVID-19 variants were considered. PRISMA recommendations were adopted for screening, eligibility, and inclusion. RESULTS: 129 unique studies were reviewed by the search criteria, of which 35 met the inclusion criteria. These comprised of 13 test negative case-control studies, 6 Phase 1-3 clinical trials, and 16 observational studies. The study location, type, vaccines used, variants considered, and reported efficacies were highlighted. CONCLUSION: Full vaccination (two doses) offers strong protection against Alpha (B.1.1.7) with 13 out of 15 studies reporting more than 84% efficacy. The results are not conclusive against the Beta (B.1.351) variant for fully vaccinated individuals with 4 out of 7 studies reporting efficacies between 22 and 60% and 3 out of 7 studies reporting efficacies between 75 and 100%. Protection against Gamma (P.1) variant was lower than 50% according to two studies in fully vaccinated individuals. The data on Delta (B.1.617.2) variant is limited but indicates lower protection compared to other variants.

Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer's disease?

A rapid increase in the number of patients with Alzheimer's disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD. For our analysis, we studied 307 participants with MCI across four years using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using a binary logistic regression, we compared episodic-memory tests to each other and to prominent neuroimaging methods in MCI converter (MCI participants who developed AD) and MCI non-converter groups (MCI participants who did not develop AD). We also combined variables to test the accuracy of mixed-predictor models. Our results indicated that the best predictors of MCI to AD conversion were the following: a combined episodic-memory and neuroimaging model in year one (59.8%), the Rey Auditory Verbal Learning Test in year two (71.7%), a mixed episodic-memory predictor model in year three (77.7%) and the Logical Memory Test in year four (77.2%) of ADNI. Overall, we found that individual episodic-memory measure and mixed models performed similarly when predicting MCI to AD conversion. Comparatively, individual neuroimaging measures predicted MCI conversion worse than chance. Accordingly, our results indicate that episodic-memory tests could be instrumental in detecting early-stage AD and enabling effective treatment.

Mechanistic insights into TNFR1/MADD death domains in Alzheimer's disease through conformational molecular dynamic analysis.

Proteins are tiny players involved in the activation and deactivation of multiple signaling cascades through interactions in cells. The TNFR1 and MADD interact with each other and mediate downstream protein signaling pathways which cause neuronal cell death and Alzheimer's disease. In the current study, a molecular docking approach was employed to explore the interactive behavior of TNFR1 and MADD proteins and their role in the activation of downstream signaling pathways. The computational sequential and structural conformational results revealed that Asp400, Arg58, Arg59 were common residues of TNFR1 and MADD which are involved in the activation of downstream signaling pathways. Aspartic acid in negatively charged residues is involved in the biosynthesis of protein. However, arginine is a positively charged residue with the potential to interact with oppositely charged amino acids. Furthermore, our molecular dynamic simulation results also ensured the stability of the backbone of TNFR1 and MADD death domains (DDs) in binding interactions. This DDs interaction mediates some conformational changes in TNFR1 which leads to the activation of mediators proteins in the cellular signaling pathways. Taken together, a better understanding of TNFR1 and MADD receptors and their activated signaling cascade may help treat Alzheimer's disease. The death domains of TNFR1 and MADD could be used as a novel pharmacological target for the treatment of Alzheimer's disease by inhibiting the MAPK pathway.

Neural Substrates of the Drift-Diffusion Model in Brain Disorders.

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

A longitudinal study using latent curve models of groups with mild cognitive impairment and Alzheimer's disease.

BACKGROUND: This study explores how mild cognitive impairment (MCI) and Alzheimer's disease (AD) develop over time. NEW METHOD: this study involves a new application of latent curve models (LCM) to examine the development trajectory of a healthy, MCI, and AD groups on a series of clinical and neural measures. Multiple-group latent curve models were used to compare the parameters of the trajectories across groups. RESULTS: LCM results showed that a linear functional form of growth was adequate for all the clinical and neural measures. Positive and significant differences in initial levels were seen across groups on all of the clinical and neural measures. In all groups, the following measures increased slightly, or considerably, over time: Clinical Dementia Rating, Alzheimer's disease Cognitive Assessment, and Montreal Assessment Test for Dementia. In contrast, a slight or a greatly decreasing trajectory was observed on the following measures: Fluorodeoxyglucose, Mini-Mental State Exam, Rey Auditory Verbal Learning Test as well as Hippocampus, Fusiform and Entorhinal Cortex volume measures. However, a constant mean trajectory was seen on Cognition Self Report Memory and languages scores. COMPARISION WITH EXISTING METHODS: there are no prior studies that applied LCM on large AD datasets. CONCLUSIONS: cognitive decline occurs in the cognitively normal (CN), MCI, and AD groups but at different rates. Further, some important cognitive, neural, and clinical variables that (a) best differentiate between CN, MCI, and AD as well as (b) differentially change over time in MCI and AD, which may explain disease progression.

Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: A systematic review.

BACKGROUND AND AIMS: To undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity. METHODS: A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19). We developed an automated search engine to screen a total of 59,000 articles in a few seconds. Filtering of the articles was then undertaken using keywords and questions, e.g. "Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?". The search terms were repeated for all the comorbidities considered in this paper. Additional articles were retrieved by searching via Google Scholar and PubMed. FINDINGS: A total of 54 articles were considered for a full review. It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity. Other comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus. CONCLUSION: Reports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself. Further research regarding this relationship and its clinical management is warranted.

The Application of Unsupervised Clustering Methods to Alzheimer's Disease.

Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data. In this paper, we focus on studying and reviewing clustering methods that have been applied to datasets of neurological diseases, especially Alzheimer's disease (AD). The aim is to provide insights into which clustering technique is more suitable for partitioning patients of AD based on their similarity. This is important as clustering algorithms can find patterns across patients that are difficult for medical practitioners to find. We further discuss the implications of the use of clustering algorithms in the treatment of AD. We found that clustering analysis can point to several features that underlie the conversion from early-stage AD to advanced AD. Furthermore, future work can apply semi-clustering algorithms on AD datasets, which will enhance clusters by including additional information.

Computational investigation of mechanistic insights of Aß42 interactions against extracellular domain of nAChRα7 in Alzheimer's disease.

AIM: Amyloid beta (Aß) 1-42, which is a basic constituent of amyloid plaques, binds with extracellular transmembrane receptor nicotine acetylcholine receptor α7 (nAChRα7) in Alzheimer's disease. MATERIALS AND METHODS: In the current study, a computational approach was employed to explore the active binding sites of nAChRα7 through Aß 1-42 interactions and their involvement in the activation of downstream signalling pathways. Sequential and structural analyses were performed on the extracellular part of nAChRα7 to identify its core active binding site. RESULTS: Results showed that a conserved residual pattern and well superimposed structures were observed in all nAChRs proteins. Molecular docking servers were used to predict the common interactive residues in nAChRα7 and Aß1-42 proteins. The docking profile results showed some common interactive residues such as Glu22, Ala42 and Trp171 may consider as the active key player in the activation of downstream signalling pathways. Moreover, the signal communication and receiving efficacy of best-docked complexes was checked through DynOmic online server. Furthermore, the results from molecular dynamic simulation experiment showed the stability of nAChRα7. The generated root mean square deviations and fluctuations (RMSD/F), solvent accessible surface area (SASA) and radius of gyration (Rg) graphs of nAChRα7 also showed its backbone stability and compactness, respectively. CONCLUSION: Taken together, our predicted results intimated the structural insight on the molecular interactions of beta amyloid protein involved in the activation of nAChRα7 receptor. In future, a better understanding of nAChRα7 and their interconnected proteins signalling cascade may be consider as target to cure Alzheimer's disease.

Improving the Detection of Protein Complexes by Predicting Novel Missing Interactome Links in the Protein-Protein Interaction Network.

Identifying protein complexes within a protein-protein interaction (PPI) networks is a crucial task in computational biology that helps to facilitate a better understanding of the cellular mechanisms it is possible to observe in various organisms. Datasets of predicted PPIs have been determined using high-throughput experimental technology. However, the datasets typically contain many spurious interactions. It is essential that these interactions, observed in the given datasets, are validated before they are employed to predict protein complexes. This paper describes the identification of missing interactome links in the PPI network as a way of improving the detection of protein complexes. The missing links have been identified by extracting several topological features. These are subsequently employed in conjunction with a two-class boosted decision-tree classifier to develop a machine-learning model that is capable of distinguishing between existing and non-existing interactome links. The model was trained on a PPI network that consisted of 1,622 proteins and 9,074 interactions, then tested on another PPI network that consisted of 1,430 proteins and 6,531 interactions. All 6,531 interactions were identified with a precision of 0.994 and a recall of 1. The model was also able to detect 37 novel interactions that were then validated using a STRING database of known and predicted PPIs. The detection of the protein complexes using CIusterONE was improved by the inclusion of the 37 novel interactions.

Molecular Docking and Dynamic Simulation of AZD3293 and Solanezumab Effects Against BACE1 to Treat Alzheimer's Disease.

The design of novel inhibitors to target BACE1 with reduced cytotoxicity effects is a promising approach to treat Alzheimer's disease (AD). Multiple clinical drugs and antibodies such as AZD3293 and Solanezumab are being tested to investigate their therapeutical potential against AD. The current study explores the binding pattern of AZD3293 and Solanezumab against their target proteins such as ß-secretase (BACE1) and mid-region amyloid-beta (Aß) (PDBIDs: 2ZHV & 4XXD), respectively using molecular docking and dynamic simulation (MD) approaches. The molecular docking results show that AZD3293 binds within the active region of BACE1 by forming hydrogen bonds against Asp32 and Lys107 with distances 2.95 and 2.68 Å, respectively. However, the heavy chain of Solanezumab interacts with Lys16 and Asp23 of amyloid beta having bond length 2.82, 2.78, and 3.00 Å, respectively. The dynamic cross correlations and normal mode analyses show that BACE1 depicted good residual correlated motions and fluctuations, as compared to Solanezumab. Using MD, the Root Mean Square Deviation and Fluctuation (RMSD/F) graphs show that AZD3293 residual fluctuations and RMSD value (0.2 nm) was much better compared to Solanezumab (0.7 nm). Moreover, the radius of gyration (Rg) results also depicts the significance of AZD3293 docked complex compared to Solanezumab through residual compactness. Our comparative results show that AZD3293 is a better therapeutic agent for treating AD than Solanezumab.

Exploring the mechanistic insights of Cas scaffolding protein family member 4 with protein tyrosine kinase 2 in Alzheimer's disease by evaluating protein interactions through molecular docking and dynamic simulations.

Cas scaffolding protein family member 4 and protein tyrosine kinase 2 are signaling proteins, which are involved in neuritic plaques burden, neurofibrillary tangles, and disruption of synaptic connections in Alzheimer's disease. In the current study, a computational approach was employed to explore the active binding sites of Cas scaffolding protein family member 4 and protein tyrosine kinase 2 proteins and their significant role in the activation of downstream signaling pathways. Sequential and structural analyses were performed on Cas scaffolding protein family member 4 and protein tyrosine kinase 2 to identify their core active binding sites. Molecular docking servers were used to predict the common interacting residues in both Cas scaffolding protein family member 4 and protein tyrosine kinase 2 and their involvement in Alzheimer's disease-mediated pathways. Furthermore, the results from molecular dynamic simulation experiment show the stability of targeted proteins. In addition, the generated root mean square deviations and fluctuations, solvent-accessible surface area, and gyration graphs also depict their backbone stability and compactness, respectively. A better understanding of CAS and their interconnected protein signaling cascade may help provide a treatment for Alzheimer's disease. Further, Cas scaffolding protein family member 4 could be used as a novel target for the treatment of Alzheimer's disease by inhibiting the protein tyrosine kinase 2 pathway.

Integration of genome-wide expression and methylation data: relevance to aging and Alzheimer's disease.

The progressive and latent nature of neurodegenerative diseases, such as Alzheimer's disease (AD) indicates the role of epigenetic modification in disease susceptibility. Previous studies from our lab show that developmental exposure to lead (Pb) perturbs the expression of AD-associated proteins. In order to better understand the role of DNA methylation as an epigenetic modifications mechanism in gene expression regulation, an integrative study of global gene expression and methylation profiles is essential. Given the different formats of gene expression and methylation data, combining these data for integrative analysis can be challenging. In this paper we describe a method to integrate and analyze gene expression and methylation arrays. Methylation array raw data contain the signal intensities of each probe of CpG sites, whereas gene expression data measure the signal intensity values of genes. In order to combine these data, methylation data of CpG sites have to be associated with genes.

Genome-wide expression and methylation profiling in the aged rodent brain due to early-life Pb exposure and its relevance to aging.

In this study, we assessed global gene expression patterns in adolescent mice exposed to lead (Pb) as infants and their aged siblings to identify reprogrammed genes. Global expression on postnatal day 20 and 700 was analyzed and genes that were down- and up-regulated (≥2 fold) were identified, clustered and analyzed for their relationship to DNA methylation. About 150 genes were differentially expressed in old age. In normal aging, we observed an up-regulation of genes related to the immune response, metal-binding, metabolism and transcription/transduction coupling. Prior exposure to Pb revealed a repression in these genes suggesting that disturbances in developmental stages of the brain compromise the ability to defend against age-related stressors, thus promoting the neurodegenerative process. Overexpression and repression of genes corresponded with their DNA methylation profile.